In [1]:
import pandas as pd
import numpy as np
from glob import glob
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
from PIL import Image
plt.style.use('ggplot')
In [2]:
annot = pd.read_parquet('./data/annot.parquet')
imgs = pd.read_parquet('./data/img.parquet')
img_fns=glob('./data/train_images/*')
In [3]:
annot.head()
Out[3]:
| id | image_id | bbox | utf8_string | points | area | |
|---|---|---|---|---|---|---|
| 0 | a4ea732cd3d5948a_1 | a4ea732cd3d5948a | [525.83, 3.4, 197.64, 33.94] | Performance | [525.83, 3.4, 723.47, 7.29, 722.76, 36.99, 525... | 6707.90 |
| 1 | a4ea732cd3d5948a_2 | a4ea732cd3d5948a | [534.67, 64.68, 91.22, 38.19] | Sport | [535.73, 64.68, 623.41, 67.51, 625.89, 102.87,... | 3483.69 |
| 2 | a4ea732cd3d5948a_3 | a4ea732cd3d5948a | [626.95, 63.62, 96.52, 31.82] | Watch | [626.95, 63.62, 721.7, 63.62, 723.47, 95.44, 6... | 3071.27 |
| 3 | a4ea732cd3d5948a_4 | a4ea732cd3d5948a | [577.4, 141.87, 147.13, 43.1] | ...period. | [580.02, 143.61, 724.53, 141.87, 723.66, 184.9... | 6341.30 |
| 4 | a4ea732cd3d5948a_5 | a4ea732cd3d5948a | [391.03, 163.9, 60.82, 38.65] | . | [395.2, 163.9, 451.85, 191.94, 445.59, 202.55,... | 2350.69 |
In [4]:
imgs.head()
Out[4]:
| id | width | height | set | file_name | |
|---|---|---|---|---|---|
| 0 | a4ea732cd3d5948a | 840 | 1024 | train | train/a4ea732cd3d5948a.jpg |
| 1 | 4bf43a7b2a898044 | 1024 | 683 | train | train/4bf43a7b2a898044.jpg |
| 2 | 1b55b309b0f50d02 | 1024 | 683 | train | train/1b55b309b0f50d02.jpg |
| 3 | 00c359f294f7dcd9 | 1024 | 680 | train | train/00c359f294f7dcd9.jpg |
| 4 | 04b5a37f762b0f51 | 768 | 1024 | train | train/04b5a37f762b0f51.jpg |
In [5]:
glob('./data/train_images/*')
Out[5]:
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In [6]:
img_id= img_fns[0].split('/')[-1].split('\\')[-1].split('.')[0]
img_id
Out[6]:
'0000599864fd15b3'
In [7]:
fig , ax = plt.subplots(figsize = (10,10))
ax.imshow(plt.imread(img_fns[0]))
ax.axis('off')
plt.show()
In [8]:
plt.imshow(plt.imread(img_fns[0]))
Out[8]:
<matplotlib.image.AxesImage at 0x27bf3fc6750>
In [9]:
image_id = img_fns[1].split('/')[-1].split('\\')[-1].split('.')[0]
len(annot.query('image_id == @image_id'))
Out[9]:
5
In [10]:
image_id3 = img_fns[0].split('/')[-1].split('\\')[-1].rstrip('jpg')
image_id3
Out[10]:
'0000599864fd15b3.'
In [11]:
fig , axs = plt.subplots(5,5 , figsize = (20,20))
axs = axs.flatten()
for i in range(25):
axs[i].imshow(plt.imread(img_fns[i]))
axs[i].axis('off')
image_id = img_fns[i].split('/')[-1].split('\\')[-1].split('.')[0]
n_annot = len(annot.query('image_id == @image_id'))
axs[i].set_title(f'{image_id} - {n_annot}')
plt.show()
Method 1: pytesseract¶
In [13]:
import pytesseract
pytesseract.pytesseract.tesseract_cmd = r'C:\Program Files\Tesseract-OCR\tesseract.exe'
for i in range (25):
print(pytesseract.image_to_string(img_fns[i], lang='eng'))
Applications as TF @ LightSabre ScreenSnap Sports Trac... Adobe PDF Touch Guitar Touch Piano Ec e UpCode YouTube ewe TfL Map WinRaR Mail for Exc... Le We oom || || _=——==- _— Options Back NISSAN GENISS »MACAU see A ee BARA ye : . ji ia ooh Le ait = 8, W§6-35- bh SU CES Suns L25P TriCKS & TiO ' wane 1 prio 1 cain ++ 12(FON 2 FON MS BIA MAL IREDMUNEE ANE FON 8 a Fora RE: SRNR IA Sen ron te meRERRTOTTARM Fmebmme “AMI BcToron 159. » FON 2M Cub 07 BEAM" FON RATA ABS, FAL + FON EPMA AS HU FONoro ma EMDR MIA FON ER» FIM La Foro B48» ATE 5000 La 2 5.mR HO RAMI RI FONT I | HEE Ii aR AE * ile WU SER Wi-Fi iL BE SRE 2 AAD FON B f@ Windows 3% BAR HD #42 FON it Bt WWW.ST oils an. ATO 8seeeeeaen
In [14]:
fig , ax = plt.subplots(figsize = (10,10))
ax.imshow(plt.imread(img_fns[4]))
ax.axis('off')
plt.show()
Method 2: easyocr¶
In [15]:
import easyocr
reader = easyocr.Reader(['en'], gpu = False)
Using CPU. Note: This module is much faster with a GPU.
In [16]:
results = reader.readtext(img_fns[11])
In [17]:
results
Out[17]:
[([[163, 211], [211, 211], [211, 249], [163, 249]], 'en', 0.7877655759729978), ([[43.217272924571674, 165.05127422674474], [171.97572614647171, 202.4608367386642], [154.7827270754283, 252.94872577325526], [27.02427385352828, 215.5391632613358]], 'Heinel', 0.5499046812577776), ([[270.32140589354253, 216.0462630228603], [413.99271136638754, 252.730123709925], [400.67859410645747, 298.95373697713967], [256.00728863361246, 261.269876290075]], 'Helneken |', 0.40450082220534445), ([[484.052175279236, 276.11391864486745], [556.8298581594385, 288.84579617112814], [549.9478247207641, 324.88608135513255], [476.17014184056154, 313.15420382887186]], 'Meken ', 0.16401357538109215), ([[588.1675694221406, 297.0875762840607], [682.973997091892, 318.54464616655406], [673.8324305778594, 351.9124237159393], [579.026002908108, 330.45535383344594]], 'Feheken ', 0.03562166001871934)]
In [ ]:
pd.DataFrame(results , columns = ['bbox' , 'text' , 'conf'])
Method 3: keras_ocr¶
In [19]:
import keras_ocr
pipeline = keras_ocr.pipeline.Pipeline()
WARNING:tensorflow:From C:\Users\gaetanyossa\AppData\Local\anaconda3\Lib\site-packages\keras\src\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead. WARNING:tensorflow:From C:\Users\gaetanyossa\AppData\Local\anaconda3\Lib\site-packages\keras\src\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead. Looking for C:\Users\gaetanyossa\.keras-ocr\craft_mlt_25k.h5 WARNING:tensorflow:From C:\Users\gaetanyossa\AppData\Local\anaconda3\Lib\site-packages\keras\src\layers\normalization\batch_normalization.py:979: The name tf.nn.fused_batch_norm is deprecated. Please use tf.compat.v1.nn.fused_batch_norm instead. WARNING:tensorflow:From C:\Users\gaetanyossa\AppData\Local\anaconda3\Lib\site-packages\tensorflow\python\util\dispatch.py:1260: resize_bilinear (from tensorflow.python.ops.image_ops_impl) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.image.resize(...method=ResizeMethod.BILINEAR...)` instead. WARNING:tensorflow:From C:\Users\gaetanyossa\AppData\Local\anaconda3\Lib\site-packages\keras\src\optimizers\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead. Looking for C:\Users\gaetanyossa\.keras-ocr\crnn_kurapan.h5 Downloading C:\Users\gaetanyossa\.keras-ocr\crnn_kurapan.h5
In [23]:
results = pipeline.recognize([img_fns[11]])
1/1 [==============================] - 7s 7s/step 1/1 [==============================] - 0s 272ms/step
In [25]:
pd.DataFrame(results[0] , columns = ['text','bbox'])
Out[25]:
| text | bbox | |
|---|---|---|
| 0 | heine | [[46.34831, 175.15732], [163.64046, 202.22473]... |
| 1 | n | [[167.0, 214.0], [208.0, 214.0], [208.0, 245.0... |
| 2 | heineken | [[273.5733, 224.26021], [406.00262, 256.36426]... |
| 3 | neken | [[489.05884, 277.76474], [553.05884, 293.76474... |
| 4 | saineken | [[591.35754, 298.9629], [678.75757, 321.76288]... |
| 5 | e | [[345.0, 647.0], [355.0, 647.0], [355.0, 664.0... |
In [26]:
keras_ocr.tools.drawAnnotations(plt.imread(img_fns[11]), results[0])
Out[26]:
<Axes: >
Plot Results: easyocr vs keras_ocr¶
In [32]:
def plot_compare(img_fn, easyocr_df, kerasocr_df):
img_id = img_fn.split('/')[-1].split('.')[0]
fig, axs = plt.subplots(1, 2, figsize=(15, 10))
easy_results = easyocr_df.query('img_id == @img_id')[['text','bbox']].values.tolist()
easy_results = [(x[0], np.array(x[1])) for x in easy_results]
keras_ocr.tools.drawAnnotations(plt.imread(img_fn),
easy_results, ax=axs[0])
axs[0].set_title('easyocr results', fontsize=24)
keras_results = kerasocr_df.query('img_id == @img_id')[['text','bbox']].values.tolist()
keras_results = [(x[0], np.array(x[1])) for x in keras_results]
keras_ocr.tools.drawAnnotations(plt.imread(img_fn),
keras_results, ax=axs[1])
axs[1].set_title('keras_ocr results', fontsize=24)
plt.show()
In [33]:
# Loop over results
for img_fn in img_fns[:25]:
plot_compare(img_fn, easyocr_df, kerasocr_df)
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